期刊
NEUROIMAGE-CLINICAL
卷 31, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2021.102768
关键词
Arteriolosclerosis; Brain; MRI; Pathology; Machine learning; Cognition
类别
资金
- NIH [P30AG010161, UH2NS100599, UH3NS100599, R01AG064233, R01AG15819, RF1AG022018, R01AG056405, R01AG17917, R01AG067482]
- Illinois Department of Public Health (Alzheimer's Disease Research Fund) - Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Eisai
- Elan Pharma-ceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- Fujirebio
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Merck Co., Inc.
- Meso Scale Diagnostics
- NeuroRx Research
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
- ADNI clinical sites in Canada
- Foundation for the National Institutes of Health
- Northern California Institute for Research and Education
- Laboratory for NeuroImaging at the University of Southern California
The study developed an in-vivo classifier of arteriolosclerosis based on brain MRI, named ARTS, showing good predictive performance and associations with cognitive decline in non-demented older adults.
Brain arteriolosclerosis, one of the main pathologies of cerebral small vessel disease, is common in older adults and has been linked to lower cognitive and motor function and higher odds of dementia. In spite of its frequency and associated morbidity, arteriolosclerosis can only be diagnosed at autopsy. Therefore, the purpose of this work was to develop an in-vivo classifier of arteriolosclerosis based on brain MRI. First, an ex-vivo classifier of arteriolosclerosis was developed based on features related to white matter hyperintensities, diffusion anisotropy and demographics by applying machine learning to ex-vivo MRI and pathology data from 119 participants of the Rush Memory and Aging Project (MAP) and Religious Orders Study (ROS), two longitudinal cohort studies of aging that recruit non-demented older adults. The ex-vivo classifier showed good performance in predicting the presence of arteriolosclerosis, with an average area under the receiver operating characteristic curve AUC = 0.78. The ex-vivo classifier was then translated to in-vivo based on available in-vivo and ex-vivo MRI data on the same participants. The in-vivo classifier was named ARTS (short for ARTerioloSclerosis), is fully automated, and provides a score linked to the likelihood a person suffers from arteriolosclerosis. The performance of ARTS in predicting the presence of arteriolosclerosis in-vivo was tested in a separate, 91% dementia-free group of 79 MAP/ROS participants and exhibited an AUC = 0.79 in persons with antemortem intervals shorter than 2.4 years. This level of performance in mostly non-demented older adults is notable considering that arterio-losclerosis can only be diagnosed at autopsy. The scan-rescan reproducibility of the ARTS score was excellent, with an intraclass correlation of 0.99, suggesting that application of ARTS in longitudinal studies may show high sensitivity in detecting small changes. Finally, higher ARTS scores in non-demented older adults were associated with greater decline in cognition two years after baseline MRI, especially in perceptual speed which has been linked to arteriolosclerosis and small vessel disease. This finding was shown in a separate group of 369 non- demented MAP/ROS participants and was validated in 72 non-demented Black participants of the Minority Aging Research Study (MARS) and also in 244 non-demented participants of the Alzheimer's Disease Neuroimaging Initiative 2 and 3. The results of this work suggest that ARTS may have broad implications in the advancement of diagnosis, prevention and treatment of arteriolosclerosis. ARTS is publicly available at https://www.nitrc.org/ projects/arts/.
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